Navegando por Orientadores "SILVEIRA, Antonio da Silva"

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Acesso aberto (Open Access)
Analysis of classical and advanced control techniques tuned with reinforcement learning
(Universidade Federal do Pará, 2023-09-01) SILVA, Daniel Abreu Macedo da; SILVEIRA, Antonio da Silva; http://lattes.cnpq.br/1828468407562753
Control theory is used to stabilize systems and obtain specific responses for each type of process. Classic controllers, such as the PID used in this research, are spread globally in industries because they have well studied topologies in the literature and are easily applied in microcontrollers or programmable lógic devices; advanced ones,such as GMV, GPC and LQR, also used in this work, have some resistance in common applications in base industries, but are widely used in energy, aerospace and robotic systems, since the complexity and structure of these methods generate robustness and reach satisfactory performances for processes that are difficult to control. In this work, these methods are studied and evaluated with a tuning approach that uses re inforcement learning. The tuning methods are used in two forms and are applied to the controllers, these are the Repeat and Improve method and the Differential Games method. The first works using offline iterations, where the process agent is the chosen control technique, which selects performance and robustness indexes as an environment (metric of how the process is evolving), being able to organize an adjustment policy for the controller, which is based on rewarding the weighting factor until reaching the process stopping criterion (desired response). The second method uses reinforcement strategies that reward the controller as the response changes, so the LQR learns the ideal control policies, adapting to changes in the environment, which allows for better performance by recalculating the traditional gains found. With the Ricatti equation for tuning the regulator; in this method, differential games are used as a framework to model and analyze dynamic systems with multiple agents. To validate what is presented, the Tachogenerator Motor and the Ar Drone have been chosen. The Tachogenerator Motor is modeled with least squares estimation in an ARX-SISO topology, in order to evaluate the first tuning method. The Ar Drone is modeled with a state space approach to evaluate the second tuning method.
Acesso aberto (Open Access)
Augmentação estocástica com horizonte de predição estendido baseada no PID para um sistema multivariável
(Universidade Federal do Pará, 2019-10-25) CRUZ, Jahyrahã Leal dos Santos; SILVEIRA, Antonio da Silva; http://lattes.cnpq.br/1828468407562753
The objective of this research was to investigate and design a control system based on the Stochastic Augmentation with Extended Prediction Horizon using 10-steps ahead, consisting of the union of characteristics of a linear deterministic controller with a stochastic predictive controller, resulting in a control system with guaranteed robustness and with predictive, linear, and stochastic characteristics. For the application of the Stochastic Augmentation, the chosen controllers were the classic PID and the GMV in its incremental form, where the former was augmented resulting in a controller with extended prediction horizon, the AEHP. The classic PID controller in the discrete time domain is compared to AEHP. Both controllers were tested in simulations with a process model that represents the dynamics of a helicopter, denominated 2DOF Helicopter (H2DOF), produced by the Quanser company. The H2DOF is a multivariable system, whose model in the state space is transformed to the transfer function form, generating two coupled subsystems, one for the pitch angle and other to the yaw angle, in which the couplings influence were considered as disturbances in the controllers design stage. The transformation of the system model to the transfer function form reduced the complexity of multivariable system in the state space, allowing the use of a more simple control law. Furthermore, it was performed the pairing of input and output, to verify what output was more sensible the one specific input, by means of Relative Gain Array. And to prove the control system efficiency based in the Stochastic Augmentation with extended prediction horizon, simulations were realized using the software Matlab®, assessing the performance of extended prediction horizon, enduring the coupled dynamics, facing load disturbances and Gaussian disturbances. The essays were evaluated by robustness and performance indices. The predictive AEHP controller obtained better results for most indices with guaranteed robustness, compared to the discrete-time PID controller.
Acesso aberto (Open Access)
Controlador GMV aplicado à regulação do ângulo de passo em um sistema de conversão de energia eólica: uma abordagem linear, preditiva e estocástica
(Universidade Federal do Pará, 2017-04-07) SILVA, Anderson de França; SILVEIRA, Antonio da Silva; http://lattes.cnpq.br/1828468407562753
In this research, we investigated linear, predictive and stochastic control techniques applied to the pitch angle control of a Wind Turbine. The topology of the Wind Energy Conversion System (WECS) studied was the Fixed-Speed Variable-Pitch. The WECS, in general, is a system that, during their operation, is subject to the constant entry of stochastic disturbances. This type of disturbance is often neglected, which will negatively affect the performance of controllers whose design was based on deterministic models. Researches developed within the Predictive and Stochastic Control Theory, have proven the benefits of performing the design of controllers based on models that consider both the deterministic and stochastic portions of the process. Following this same design approach, the Picth Angle Control (PAC) project was developed, in this dissertation, using the Stochastic Augmentation (SA) of deterministic controllers. The main objectives sought, were: to minimize the energy consumption of the control system; and to reduce the costs related to the maintenance and replacement of actuators in the pitch angle control system. Simulation tests were performed on a MathWorks® WECS simulator. The results of the tests have proved the good performance of the PAC designed through the SA, which reached the control objectives sought with the development of this research.
Acesso aberto (Open Access)
Controle linear quadrático gaussiano de um quadricóptero baseado em um filtro de Kalman estendido com variável instrumental
(Universidade Federal do Pará, 2024-02-08) SODRÉ, Lucas de Carvalho; SILVEIRA, Antonio da Silva; http://lattes.cnpq.br/1828468407562753; https://orcid.org/0000-0002-2698-2677
Given the transformations and promotion of technologies and modernization in different areas of society, such as the use of Unmanned Aerial Vehicles performing numerous automated activities, it is necessary to create algorithms with efficiency and safety to avoid losses and damages in their functions. Aerial systems are, for the most part, multiple input and multiple output systems, time-varying, susceptible to disturbances and measurement noise, becoming a challenging scenario in the area of system identification. Given this, such dynamics must be considered in the identification process. Therefore, the objective of this work is to develop an algorithm capable of jointly estimating the states and parameters of systems, mitigating the interference of measurement noise and external disturbances in the real-time identification process. Based on these principles, the creation of the joint estimation algorithm Extended Kalman Filter with Instrumental Variables was established. The proposed algorithm stands out for its theoretical commitment to minimizing interference from dynamics that can affect the reliability of parameters calculated by identification methods already consolidated in the literature, such as Extended Kalman Filter (EKF) and Recursive Least Squares (RLS). The proposed method was tested to calculate the stochastic linear model of the autopilot system of the unmanned aerial quadcopter, Parrot’s AR Drone 2.0 model, taking into account scenarios in which the sensor signal presents a signal-to-noise ratio of 100, 50, 10. Its performance was compared with RLS and EKF parameter estimation. To evaluate the state estimates, the root-mean-square deviation norm index was used and, to evaluate the parameters, the Euclidean distance between the real parameters and the estimated parameters was used. Finally, the data collected by the methods were used to tune the Gaussian Quadratic Linear Control controller, thus allowing comparison of the impact of the identification method on the closed-loop behavior of the aerial system. To enable discussion and comparison of control algorithms, the Squared Error Integral and Squared Control Integral indices were applied to evaluate the control performance, the gain margin and the phase margin to measure system robustness.
Acesso aberto (Open Access)
Controle MPC multivariável com restrições usando funções de Laguerre
(Universidade Federal do Pará, 2018-03-01) PINHEIRO, Tarcísio Carlos Farias; SILVEIRA, Antonio da Silva; http://lattes.cnpq.br/1828468407562753
This work presents a constrained multivariable model predictive controller using Laguerre Functions. This controller uses a set of orthonormal Laguerre networks for representation of the control trajectory within a control horizon. In order to demonstrate the advantages of applying this type of controller in MIMO (Multiple-Input and Multiple-Output) systems, the Laguerre Functions Functions are used to decrease the computational load used to calculate the optimal control. In addition, It improves the compromise between control signal viability and closed-loop performance of the system. The Laguerre Functions are also used in conjunction with Hildreth’s Quadratic Programming to find the optimal solution for the case where the control signal is constrained. The proposed controller presents advantages when compared to the classical model predictive control approach, where forward shift operators are used to predict the future trajectory of the control signal, leading to unsatisfactory solutions and a high computational load for cases where the control signal demands a long prediction horizon and a high closed-loop performance.It is also reported the practical testes with a robotic manipulator configured as a MIMO system with three inputs and three outputs and tests simulated with the Wood and Berry binary distillation column which is a MIMO system with two inputs and two outputs, also containing transport time delays. The tests aim to compare the controller results presented with the traditional predictive control approach and thereby demonstrate the advantages of the method using the Laguerre functions and their efficiency for MIMO systems.
Acesso aberto (Open Access)
Metodologia para estimação de intenção de movimento e controle em tempo real de prótese mioelétrica de mão: uma abordagem linear, preditiva e estocástica
(Universidade Federal do Pará, 2018-03-28) DUTRA, Bruno Gomes; SILVEIRA, Antonio da Silva; http://lattes.cnpq.br/1828468407562753
Muscle signals from electromyography (EMG) are widely used to detect muscle contraction and intention to motion. By using these signals in real time in prosthetic control, a low signal to noise ratio is commonly found. Thus, it is necessary to have recursive methods, robust to noise and efficient algorithms, to generate commands in real time for the robotic actuator. In this research, stochastic system indentification techniques, Kalman filter, sensor fusion and stochastic predictive control techniques were investigated and applied to improve the measurement and processing of electromyographic signals to increase robustness in the control of biomechatronic prostheses. Thus, it is an improved process, less sensitive to noise and with minimal delays and phase lags. In this methodology, a four-stage distribution method is used: (1) features extraction by using an autoregressive model (AR), (2) data fusion with the Kalman filter, (3) motion estimation algorithm, and (4) predictive control with the generalized minimum variance controller applied to a servomechanism. The main objectives were: to enhance the signal-to-noise ratio of EMG signals, to have a low-cost real-time processing man-machine interface, to avoid measurement problems and to minimize energy consumption of the control system. A didactic plant was developed, which is a 4 channel EMG data acquisition and processing system with a servomechanism and its control system coupled in a robotic jaw. Practical tests were conducted with the prototype and the results show that it is possible to continuously estimate the intention of opening and closing movement of the hand and can confirm the good performance of the stochastic controller designed for the control of the electric prosthesis.
Acesso aberto (Open Access)
Projeto de estabilizadores de sistemas elétricos de potência utilizando controle de variância mínima no espaço de estados
(Universidade Federal do Pará, 2018-01-24) CASTRO, Luís Augusto Mesquita de; ARAÚJO, Rejane de Barros; http://lattes.cnpq.br/8760830024389437; SILVEIRA, Antonio da Silva; http://lattes.cnpq.br/1828468407562753
The use of power system stabilizers is essential for reliable operation of large electrical systems. Most stabilizers in operation are designed using classical control techniques based on linearized power systems models. Although this type of stabilizer presents satisfactory performance for the damping of oscillations inherent in the power system, many studies show that use of adaptive and intelligent control techniques for the synthesis of the control law in these stabilizers can produce even better results. In this work it is investigated the performance of a predictive control strategy, of the minimum variance control type in the state space, GMVSS, applied to the damping of electromechanical oscillations in interconnected power systems. The design procedure is based on the premise that the controller structure is inherited from the design model, where estimated state variables, come into play in the synthesis of a state feedback control law. The complexity of the controller structure is then dictated by the complexity of the design model. This procedure differs from the original transfer function method, GMV, however matching exactly the same results. The most significant contribution of such a strategy is the simplicity of design due to the absence of the Diophantine equation in the procedure. The Diophantine equation is indirectly solved in a natural way by the problem formulation itself, from a Kalman filter obtained from an ARMAX state space representation. Finally, the synthesized control law is applied to the nonlinear system by means of numerical simulations using nonlinear models of the system, evaluating the characteristics of robustness and performance of the proposed controller via sensitivity functions, Nyquist diagram, poles and zeros map and performance indexes for the entire operating range. The results show that the predictive stabilizer is able to contribute positively to the damping of the most problematic oscillation modes, thus increasing the stability limits of the power system.
Acesso aberto (Open Access)
Sistema de automação IoT para gestão de ativos no cenário da indústria 4.0
(Universidade Federal do Pará, 2023-07-10) GOMES, Woldson Leonne Pereira; SERUFFO, Marcos César da Rocha; http://lattes.cnpq.br/3794198610723464; SILVEIRA, Antonio da Silva; http://lattes.cnpq.br/1828468407562753
The fourth industrial revolution has several pillars, with the Internet of Things and Big Data being one of the most prominent. These technologies make it possible to collect and analyze large data sets in real time, allowing the development of models for the most diverse situations, from consumer behavior to the prevention of manufacturing failures. In this context, the present work addresses the development of a complete architecture for the implementation of an automation system for Industry 4.0, at the hardware and software level, based on the collection of temperature, hour meter, vibration and current data in electric motors of a primary aluminum industry. From the measured variables, vibration data can be obtained in the frequency domain, phase imbalance and Kalman filter estimation. A multicriteria decision algorithm was adopted to assist in choosing the programming language. After elaborating this systematic, a set of solutions was obtained that made the development of the system feasible, which was validated in a controlled experimental setup. The automation system developed was called IOTCORE, which performs the collection of variables in real time, with low latency, high performance, and makes it possible to transmit, store and visualize the data in several supervisory systems.